Presently available information indicates the following: 1) More than 211,000 American women will be diagnosed with breast cancer this year; 2) Ninety two percent of late-stage breast cancer cases in the United States could have been treated if they had been detected earlier; 3) About 70 percent of women over 40 years of age had a mammogram in the last two years; 4) Mammography, in spite of its unpleasant procedure for women (breast constriction) and its radiation exposure problems, remains the only method available for breast cancer detection; and 5) Mammography shows a high rate of failure for women in the 25 to 40 year old age group who tend to have radiological dense breast.
Recently there has been considerable interest in developing alternative methods for breast cancer detection due to the inability of x-ray mammography to image radiological dense breasts as well as the low positive predictive value in such circumstances. Among various alternative methods, microwave imaging is of particular clinical interest because of the high contrast available at microwave frequencies between normal and malignant tissues. One comparative study showed that microwave imaging could offer an order of magnitude higher contrast than x-ray or ultrasound imaging techniques.
The earliest work in microwave imaging for biomedical applications known to the preset inventors was a study by Larsen and Jacobi entitled “Microwave Scattering Parameter Imagery of an Isolated Canine Kidney” (Medical Physics—September 1979—Volume 6, Issue 5, pp. 394-403). Their work was conducted at 3.9 GHz using scanning for imaging isolated organs. The microwave images they obtained showed that exact anatomy cannot be recovered but contours related to lobular organization were identifiable. These disadvantages, however, have been overcome in part through the use of reconstructive algorithms.
A first type of reconstruction algorithm, known as a Born approximation, used in microwave imaging is based on the assumption of weak scatters and is referred to as diffraction tomography. The main advantage of this algorithm is that it provides a quasi real-time reconstruction of the polarization current density distribution. Microwave images obtained based on diffraction algorithms are able to provide qualitative information about objects under investigation but they fail to provide qualitative images of electric properties, i.e. permittivity and conductivity, of object with high contrast. This failure results from an initial assumption that does not take into consideration multiple scattering but rather assumes the reconstruction problem to be a linear problem.
To consider multiple scattering, an iterative nonlinear algorithm must be used. At each iteration, an equation, e.g. a Helmholtz equation, describing electric field distribution in homogeneous media is solved. Then the electric properties are adjusted by minimizing the errors between measured and calculated electric fields. This procedure takes into account nonlinearity of the inverse scattering problem eliminating the contrast limitation of diffraction tomography as well as allowing resolution better than half wavelength to be achieved. The ultimate factors that determine the resolution of a nonlinear algorithm are the available signal-to-noise ration (SNR) and accuracy in evaluation of electric field.
Prior nonlinear reconstruction approaches have been developed including: Newton-type with Tikhonov regularization as described by Joachimowicz et al. in their article “Inverse scattering: an iterative numerical method for electromagnetic imaging,” IEEE Transactions on Antennas and Propagation, 1991 and by Joachimowicz et al. in their article “Convergence and Stability Assessment of Newton-Kantorovich Reconstruction Algorithms for Microwave Tomography,” IEEE Transactions on Medical Imaging, 1998; Newton-type with Marquardt regularization as described by Meaney et al. in their article “An active microwave imaging system reconstruction of 2-D electrical property distributions,” IEEE Transactions on Biomedical Engineering, 1995 and by Meaney et al. in their article “Pre-scaled two-parameter Gauss-Newton image reconstruction to reduce property recovery imbalance,” Physics in Medicine and Biology, April 2002; conjugate-gradient with Tikhonov regularization as described by Rekanos et al. in their article “Microwave imaging using the finite-element method and a sensitive analysis approach,” IEEE Transactions on Medical Imaging, 1999 and by Bulyshev et al. in their article “Computational modeling of three-dimensional microwave tomography of breast cancer,” IEEE Transactions on Biomedical Engineering,” 2001; and contrast source inversion methods as described by van den Berg et al. in their article “A contrast source inversion method,” Inverse Problems, 1997 and by Zhang et al, in their article “Three-dimensional nonlinear image reconstruction for microwave biomedical imaging,” IEEE Transactions on Biomedical Engineering, 2004.
While various implementations of microwave image reconstruction have been developed, and while various data refining algorithms have been devised, no design has emerged that generally encompasses all of the desired characteristics as hereafter presented in accordance with the subject technology.